Large language models (LLMs) are increasingly sophisticated and widely deployed in sensitive applications, but they pose significant risks such as bias, unsafe actions, dataset poisoning, lack of explainability, hallucinations, and non-reproducibility. These risks necessitate the development of "guardrails" to align LLMs with desired behaviors and mitigate potential harm. This paper explores the risks associated with deploying LLMs and evaluates current approaches to implementing guardrails and model alignment techniques. It discusses intrinsic and extrinsic bias evaluation methods, the importance of fairness metrics, and the safety and reliability of agentic LLMs. Technical strategies for securing LLMs include a layered protection model operating at external, secondary, and internal levels, system prompts, Retrieval-Augmented Generation (RAG) architectures, and techniques to minimize bias and protect privacy. Effective guardrail design requires a deep understanding of the LLM’s intended use case, relevant regulations, and ethical considerations. Balancing competing requirements such as accuracy and privacy remains a challenge. The paper also examines the challenges in implementing guardrails, including flexibility vs. stability, emergent complexity, unclear goals and metrics, system testability and evolvability, and cost. Open-source tools like Nemo-Guardrails, LLamaGuard, and Guardrails AI are presented as promising solutions. Despite the complexities, developing reliable safeguards is crucial for maximizing LLMs’ benefits and minimizing their potential harms. Continued research, development, and open collaboration are vital to ensure the safe, responsible, and equitable use of LLMs in the future.Large language models (LLMs) are increasingly sophisticated and widely deployed in sensitive applications, but they pose significant risks such as bias, unsafe actions, dataset poisoning, lack of explainability, hallucinations, and non-reproducibility. These risks necessitate the development of "guardrails" to align LLMs with desired behaviors and mitigate potential harm. This paper explores the risks associated with deploying LLMs and evaluates current approaches to implementing guardrails and model alignment techniques. It discusses intrinsic and extrinsic bias evaluation methods, the importance of fairness metrics, and the safety and reliability of agentic LLMs. Technical strategies for securing LLMs include a layered protection model operating at external, secondary, and internal levels, system prompts, Retrieval-Augmented Generation (RAG) architectures, and techniques to minimize bias and protect privacy. Effective guardrail design requires a deep understanding of the LLM’s intended use case, relevant regulations, and ethical considerations. Balancing competing requirements such as accuracy and privacy remains a challenge. The paper also examines the challenges in implementing guardrails, including flexibility vs. stability, emergent complexity, unclear goals and metrics, system testability and evolvability, and cost. Open-source tools like Nemo-Guardrails, LLamaGuard, and Guardrails AI are presented as promising solutions. Despite the complexities, developing reliable safeguards is crucial for maximizing LLMs’ benefits and minimizing their potential harms. Continued research, development, and open collaboration are vital to ensure the safe, responsible, and equitable use of LLMs in the future.